HDBRR: a statistical package for high-dimensional Bayesian ridge regression without MCMC

DOI10.1080/00949655.2022.2081968OpenAlexW4281643925WikidataQ114101193 ScholiaQ114101193MaRDI QIDQ5055265

Sergio Perez-Elizalde, Jose Crossa, Blanca E. Monroy-Castillo, Paulino Pérez-Rodríguez

Publication date: 13 December 2022

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2022.2081968

zbMATH Keywords

Rridge regressionregressionvariable selectionshrinkageMCMCBayesian methods

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (2)

HDBRR: a statistical package for high-dimensional Bayesian ridge regression without MCMC ⋮ HDBRR

Uses Software

• R
• Bioconductor
• penalized
• BayesDA
• BGLR
• ridge
• pi-MASS
• GEOquery
• bigparallelr
• bigstatsr

Cites Work

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• Bayesian variable selection regression for genome-wide association studies and other large-scale problems
• On the mean squared error of the ridge estimator of the covariance and precision matrix
• An introduction to MCMC for machine learning
• Ridge regression:some simulations
• Bayes Factors
• Bayesian estimation of the biasing parameter for ridge regression: A novel approach
• HDBRR: a statistical package for high-dimensional Bayesian ridge regression without MCMC
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Ridge Regression: Applications to Nonorthogonal Problems
• Bayesian Statistical Methods
• Introducing Monte Carlo Methods with R

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